Package: DSWE 1.8.4
Yu Ding
DSWE: Data Science for Wind Energy
Data science methods used in wind energy applications. Current functionalities include creating a multi-dimensional power curve model, performing power curve function comparison, covariate matching, and energy decomposition. Relevant works for the developed functions are: funGP() - Prakash et al. (2022) <doi:10.1080/00401706.2021.1905073>, AMK() - Lee et al. (2015) <doi:10.1080/01621459.2014.977385>, tempGP() - Prakash et al. (2022) <doi:10.1080/00401706.2022.2069158>, ComparePCurve() - Ding et al. (2021) <doi:10.1016/j.renene.2021.02.136>, deltaEnergy() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, syncSize() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, imptPower() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, All other functions - Ding (2019, ISBN:9780429956508).
Authors:
DSWE_1.8.4.tar.gz
DSWE_1.8.4.zip(r-4.7-x86_64)DSWE_1.8.4.zip(r-4.7-arm64)DSWE_1.8.4.zip(r-4.6-x86_64)DSWE_1.8.4.zip(r-4.6-arm64)
DSWE_1.8.4.tar.gz(r-4.7-arm64)DSWE_1.8.4.tar.gz(r-4.7-x86_64)DSWE_1.8.4.tar.gz(r-4.6-arm64)DSWE_1.8.4.tar.gz(r-4.6-x86_64)
DSWE_1.8.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
DSWE/json (API)
| # Install 'DSWE' in R: |
| install.packages('DSWE', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/tamu-aml/dswe-package/issues
Last updated from:e23c236ef0. Checks:10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 169 | ||
| linux-devel-x86_64 | OK | 173 | ||
| source / vignettes | OK | 238 | ||
| linux-release-arm64 | OK | 153 | ||
| linux-release-x86_64 | OK | 190 | ||
| windows-devel-arm64 | OK | 268 | ||
| windows-devel-x86_64 | OK | 163 | ||
| windows-release-arm64 | OK | 330 | ||
| windows-release-x86_64 | OK | 187 | ||
| wasm-release | OK | 137 |
Exports:AMKComparePCurveComputeWeightedDifferenceCovMatchdeltaEnergyfunGPimptPowerKnnPCFitKnnPredictKnnUpdateSplinePCFitSvmPCFitsyncSizetempGPupdateDataXgbPCFit
Dependencies:askpassbase64encbslibcachemclassclicpp11crosstalkcurldata.tabledigestdplyre1071evaluatefarverfastmapFNNfontawesomefsgenericsggplot2gluegssgtablehighrhtmltoolshtmlwidgetshttrisobandjquerylibjsonlitekernlabKernSmoothknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixmatrixStatsmemoisemimemixtoolsnlmeopensslotelpillarpkgconfigplotlypromisesproxypurrrR6rappdirsRColorBrewerRcppRcppArmadillorlangrmarkdownS7sassscalessegmentedstringistringrsurvivalsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxgboostyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Additive Multiplicative Kernel Regression | AMK |
| Power curve comparison | ComparePCurve |
| Percentage weighted difference between power curves | ComputeWeightedDifference |
| Covariate Matching | CovMatch |
| Wind Energy data set containing 47,542 data points | data1 |
| Wind Energy data set containing 48,068 data points | data2 |
| Energy decomposition for wind turbine performance comparison | deltaEnergy |
| Function comparison using Gaussian Process and Hypothesis testing | funGP |
| Power imputation | imptPower |
| KNN : Fit | KnnPCFit |
| KNN : Predict | KnnPredict |
| KNN : Update | KnnUpdate |
| predict from temporal Gaussian process | predict.tempGP |
| Smoothing spline Anova method | SplinePCFit |
| SVM based power curve modelling | SvmPCFit |
| Data synchronization | syncSize |
| temporal Gaussian process | tempGP |
| Updating data in a model | updateData |
| Update the data in a tempGP object | updateData.tempGP |
| xgboost based power curve modelling | XgbPCFit |